QRS complex detection and R–R interval computation based on discrete wavelet transform

IF 0.5 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal on Smart Sensing and Intelligent Systems Pub Date : 2020-01-01 DOI:10.21307/ijssis-2020-010

Aqeel M. Hamad alhussainy

引用次数: 3

Abstract

Abstract QRS represented the most important part of ECG signal, so different researches and studies are performed for QRS recognition. In this paper, a new technique by using wavelet transform is used for de-noising ECG signal by using adaptive threshold, then DWT used to separate the high frequency from the low component, then compute the statistical information from low frequencies to be used in threshold computation, Based on these statics features, lower and upper threshold are calculated, which are updated according to number of peaks that are detected until two thresholds give same number of peaks, also the detected peaks are updated according to average R–R time. Results of (EDB) database was (Acc = 99.366%), while (LTSTDB) database was (Acc = 98.89%). The results are compared with other work and it is show that the proposed method gave better performance and can be used for QRS detection.

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基于离散小波变换的QRS复合体检测及R-R区间计算

QRS是心电信号中最重要的部分，因此对QRS的识别进行了不同的研究。本文采用小波变换对心电信号进行自适应阈值降噪，然后采用小波变换对高频分量和低分量进行分离，计算出用于阈值计算的低频统计信息，根据这些静态特征计算出下阈值和上阈值，并根据检测到的峰值数更新下阈值和上阈值，直到两个阈值给出相同的峰值数。根据平均R-R时间更新检测到的峰值。(EDB)数据库结果为(Acc = 99.366%)， (LTSTDB)数据库结果为(Acc = 98.89%)。结果表明，该方法具有较好的性能，可用于QRS检测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal on Smart Sensing and Intelligent Systems ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

2.70

自引率

8.30%

发文量

审稿时长

8 weeks

期刊介绍： nternational Journal on Smart Sensing and Intelligent Systems (S2IS) is a rapid and high-quality international forum wherein academics, researchers and practitioners may publish their high-quality, original, and state-of-the-art papers describing theoretical aspects, system architectures, analysis and design techniques, and implementation experiences in intelligent sensing technologies. The journal publishes articles reporting substantive results on a wide range of smart sensing approaches applied to variety of domain problems, including but not limited to: Ambient Intelligence and Smart Environment Analysis, Evaluation, and Test of Smart Sensors Intelligent Management of Sensors Fundamentals of Smart Sensing Principles and Mechanisms Materials and its Applications for Smart Sensors Smart Sensing Applications, Hardware, Software, Systems, and Technologies Smart Sensors in Multidisciplinary Domains and Problems Smart Sensors in Science and Engineering Smart Sensors in Social Science and Humanity